IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v85y2020i3d10.1007_s11336-020-09717-2.html
   My bibliography  Save this article

Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models

Author

Listed:
  • Shiyu Wang

    (University of Georgia)

  • Yinghan Chen

    (University of Nevada, Reno)

Abstract

The recent “Every Student Succeed Act" encourages schools to use an innovative assessment to provide feedback about students’ mastery level of grade-level content standards. Mastery of a skill requires the ability to complete the task with not only accuracy but also fluency. This paper offers a new sight on using both response times and response accuracy to measure fluency with cognitive diagnosis model framework. Defining fluency as the highest level of a categorical latent attribute, a polytomous response accuracy model and two forms of response time models are proposed to infer fluency jointly. A Bayesian estimation approach is developed to calibrate the newly proposed models. These models were applied to analyze data collected from a spatial rotation test. Results demonstrate that compared with the traditional CDM that using response accuracy only, the proposed joint models were able to reveal more information regarding test takers’ spatial skills. A set of simulation studies were conducted to evaluate the accuracy of model estimation algorithm and illustrate the various degrees of model complexities.

Suggested Citation

  • Shiyu Wang & Yinghan Chen, 2020. "Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 600-629, September.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09717-2
    DOI: 10.1007/s11336-020-09717-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-020-09717-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-020-09717-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    2. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    3. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
    4. Gunter Maris & Han Maas, 2012. "Speed-Accuracy Response Models: Scoring Rules based on Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 615-633, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peida Zhan & Xin Qiao, 2022. "DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1529-1547, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peida Zhan & Xin Qiao, 2022. "DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1529-1547, December.
    2. Yi-Hsuan Lee & Zhiliang Ying, 2015. "A Mixture Cure-Rate Model for Responses and Response Times in Time-Limit Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 748-775, September.
    3. Frederik Coomans & Abe Hofman & Matthieu Brinkhuis & Han L J van der Maas & Gunter Maris, 2016. "Distinguishing Fast and Slow Processes in Accuracy - Response Time Data," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    4. Sandip Sinharay & Peter W. van Rijn, 2020. "Assessing Fit of the Lognormal Model for Response Times," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 534-568, October.
    5. Inhan Kang & Paul Boeck & Roger Ratcliff, 2022. "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 725-748, June.
    6. Peter W. Rijn & Usama S. Ali, 2018. "A Generalized Speed–Accuracy Response Model for Dichotomous Items," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 109-131, March.
    7. M. Marsman & H. Sigurdardóttir & M. Bolsinova & G. Maris, 2019. "Characterizing the Manifest Probability Distributions of Three Latent Trait Models for Accuracy and Response Time," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 870-891, September.
    8. Matthew S. Johnson & Sandip Sinharay, 2020. "The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 5-31, February.
    9. Steven Andrew Culpepper & James Joseph Balamuta, 2017. "A Hierarchical Model for Accuracy and Choice on Standardized Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 820-845, September.
    10. Edison M. Choe & Jinming Zhang & Hua-Hua Chang, 2018. "Sequential Detection of Compromised Items Using Response Times in Computerized Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 650-673, September.
    11. Hans-Friedrich Köhn & Chia-Yi Chiu, 2018. "How to Build a Complete Q-Matrix for a Cognitively Diagnostic Test," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 273-299, July.
    12. Douglas L. Steinley & M. J. Brusco, 2019. "Using an Iterative Reallocation Partitioning Algorithm to Verify Test Multidimensionality," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 397-413, October.
    13. Guanhua Fang & Jingchen Liu & Zhiliang Ying, 2019. "On the Identifiability of Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 19-40, March.
    14. Hans-Friedrich Köhn & Chia-Yi Chiu, 2017. "A Procedure for Assessing the Completeness of the Q-Matrices of Cognitively Diagnostic Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 112-132, March.
    15. Chia-Yi Chiu & Hans-Friedrich Köhn, 2019. "Consistency Theory for the General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 830-845, September.
    16. Sora Lee & Daniel M. Bolt, 2018. "Asymmetric Item Characteristic Curves and Item Complexity: Insights from Simulation and Real Data Analyses," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 453-475, June.
    17. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    18. Yan Li & Chao Huang & Jia Liu, 2023. "Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward?," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 842-865, December.
    19. Chen-Wei Liu & Björn Andersson & Anders Skrondal, 2020. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 322-357, June.
    20. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09717-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.